{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2 Datasetの実装、 2.3 DataLoaderの実装\n",
    "\n",
    "本ファイルでは、SSDなど物体検出アルゴリズム用のDatasetとDataLoaderを作成します。\n",
    "\n",
    "VOC2012データセットを対象とします。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2 学習目標\n",
    "\n",
    "1.\t物体検出で使用するDatasetクラスを作成できるようになる\n",
    "2.\tSSDの学習時のデータオーギュメンテーションで、何をしているのかを理解する\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 学習目標\n",
    "\n",
    "1.\t物体検出で使用するDataLoaderクラスを作成できるようになる\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 事前準備\n",
    "\n",
    "\n",
    "OpenCVのインストール\n",
    "\n",
    "- pip install opencv-python\n",
    "\n",
    "本書の指示に従い、VOC2010のデータセットをダウンロード\n",
    "\n",
    "- http://host.robots.ox.ac.uk/pascal/VOC/voc2012/\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# パッケージのimport\n",
    "import os.path as osp\n",
    "import random\n",
    "# XMLをファイルやテキストから読み込んだり、加工したり、保存したりするためのライブラリ\n",
    "import xml.etree.ElementTree as ET\n",
    "\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.utils.data as data\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 乱数のシードを設定\n",
    "torch.manual_seed(1234)\n",
    "np.random.seed(1234)\n",
    "random.seed(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画像データ、アノテーションデータへのファイルパスのリストを作成する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 学習、検証の画像データとアノテーションデータへのファイルパスリストを作成する\n",
    "\n",
    "\n",
    "def make_datapath_list(rootpath):\n",
    "    \"\"\"\n",
    "    データへのパスを格納したリストを作成する。\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    rootpath : str\n",
    "        データフォルダへのパス\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ret : train_img_list, train_anno_list, val_img_list, val_anno_list\n",
    "        データへのパスを格納したリスト\n",
    "    \"\"\"\n",
    "\n",
    "    # 画像ファイルとアノテーションファイルへのパスのテンプレートを作成\n",
    "    imgpath_template = osp.join(rootpath, 'JPEGImages', '%s.jpg')\n",
    "    annopath_template = osp.join(rootpath, 'Annotations', '%s.xml')\n",
    "\n",
    "    # 訓練と検証、それぞれのファイルのID（ファイル名）を取得する\n",
    "    train_id_names = osp.join(rootpath + 'ImageSets/Main/train.txt')\n",
    "    val_id_names = osp.join(rootpath + 'ImageSets/Main/val.txt')\n",
    "\n",
    "    # 訓練データの画像ファイルとアノテーションファイルへのパスリストを作成\n",
    "    train_img_list = list()\n",
    "    train_anno_list = list()\n",
    "\n",
    "    for line in open(train_id_names):\n",
    "        file_id = line.strip()  # 空白スペースと改行を除去\n",
    "        img_path = (imgpath_template % file_id)  # 画像のパス\n",
    "        anno_path = (annopath_template % file_id)  # アノテーションのパス\n",
    "        train_img_list.append(img_path)  # リストに追加\n",
    "        train_anno_list.append(anno_path)  # リストに追加\n",
    "\n",
    "    # 検証データの画像ファイルとアノテーションファイルへのパスリストを作成\n",
    "    val_img_list = list()\n",
    "    val_anno_list = list()\n",
    "\n",
    "    for line in open(val_id_names):\n",
    "        file_id = line.strip()  # 空白スペースと改行を除去\n",
    "        img_path = (imgpath_template % file_id)  # 画像のパス\n",
    "        anno_path = (annopath_template % file_id)  # アノテーションのパス\n",
    "        val_img_list.append(img_path)  # リストに追加\n",
    "        val_anno_list.append(anno_path)  # リストに追加\n",
    "\n",
    "    return train_img_list, train_anno_list, val_img_list, val_anno_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/VOCdevkit/VOC2012/JPEGImages/2008_000008.jpg\n"
     ]
    }
   ],
   "source": [
    "# ファイルパスのリストを作成\n",
    "rootpath = \"./data/VOCdevkit/VOC2012/\"\n",
    "train_img_list, train_anno_list, val_img_list, val_anno_list = make_datapath_list(\n",
    "    rootpath)\n",
    "\n",
    "# 動作確認\n",
    "print(train_img_list[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xml形式のアノテーションデータをリストに変換する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 「XML形式のアノテーション」を、リスト形式に変換するクラス\n",
    "\n",
    "\n",
    "class Anno_xml2list(object):\n",
    "    \"\"\"\n",
    "    1枚の画像に対する「XML形式のアノテーションデータ」を、画像サイズで規格化してからリスト形式に変換する。\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    classes : リスト\n",
    "        VOCのクラス名を格納したリスト\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, classes):\n",
    "\n",
    "        self.classes = classes\n",
    "\n",
    "    def __call__(self, xml_path, width, height):\n",
    "        \"\"\"\n",
    "        1枚の画像に対する「XML形式のアノテーションデータ」を、画像サイズで規格化してからリスト形式に変換する。\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        xml_path : str\n",
    "            xmlファイルへのパス。\n",
    "        width : int\n",
    "            対象画像の幅。\n",
    "        height : int\n",
    "            対象画像の高さ。\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        ret : [[xmin, ymin, xmax, ymax, label_ind], ... ]\n",
    "            物体のアノテーションデータを格納したリスト。画像内に存在する物体数分のだけ要素を持つ。\n",
    "        \"\"\"\n",
    "\n",
    "        # 画像内の全ての物体のアノテーションをこのリストに格納します\n",
    "        ret = []\n",
    "\n",
    "        # xmlファイルを読み込む\n",
    "        xml = ET.parse(xml_path).getroot()\n",
    "\n",
    "        # 画像内にある物体（object）の数だけループする\n",
    "        for obj in xml.iter('object'):\n",
    "\n",
    "            # アノテーションで検知がdifficultに設定されているものは除外\n",
    "            difficult = int(obj.find('difficult').text)\n",
    "            if difficult == 1:\n",
    "                continue\n",
    "\n",
    "            # 1つの物体に対するアノテーションを格納するリスト\n",
    "            bndbox = []\n",
    "\n",
    "            name = obj.find('name').text.lower().strip()  # 物体名\n",
    "            bbox = obj.find('bndbox')  # バウンディングボックスの情報\n",
    "\n",
    "            # アノテーションの xmin, ymin, xmax, ymaxを取得し、0～1に規格化\n",
    "            pts = ['xmin', 'ymin', 'xmax', 'ymax']\n",
    "\n",
    "            for pt in (pts):\n",
    "                # VOCは原点が(1,1)なので1を引き算して（0, 0）に\n",
    "                cur_pixel = int(bbox.find(pt).text) - 1\n",
    "\n",
    "                # 幅、高さで規格化\n",
    "                if pt == 'xmin' or pt == 'xmax':  # x方向のときは幅で割算\n",
    "                    cur_pixel /= width\n",
    "                else:  # y方向のときは高さで割算\n",
    "                    cur_pixel /= height\n",
    "\n",
    "                bndbox.append(cur_pixel)\n",
    "\n",
    "            # アノテーションのクラス名のindexを取得して追加\n",
    "            label_idx = self.classes.index(name)\n",
    "            bndbox.append(label_idx)\n",
    "\n",
    "            # resに[xmin, ymin, xmax, ymax, label_ind]を足す\n",
    "            ret += [bndbox]\n",
    "\n",
    "        return np.array(ret)  # [[xmin, ymin, xmax, ymax, label_ind], ... ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.09      ,  0.03003003,  0.998     ,  0.996997  , 18.        ],\n",
       "       [ 0.122     ,  0.56756757,  0.164     ,  0.72672673, 14.        ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 動作確認　\n",
    "voc_classes = ['aeroplane', 'bicycle', 'bird', 'boat',\n",
    "               'bottle', 'bus', 'car', 'cat', 'chair',\n",
    "               'cow', 'diningtable', 'dog', 'horse',\n",
    "               'motorbike', 'person', 'pottedplant',\n",
    "               'sheep', 'sofa', 'train', 'tvmonitor']\n",
    "\n",
    "transform_anno = Anno_xml2list(voc_classes)\n",
    "\n",
    "# 画像の読み込み OpenCVを使用\n",
    "ind = 1\n",
    "image_file_path = val_img_list[ind]\n",
    "img = cv2.imread(image_file_path)  # [高さ][幅][色BGR]\n",
    "height, width, channels = img.shape  # 画像のサイズを取得\n",
    "\n",
    "# アノテーションをリストで表示\n",
    "transform_anno(val_anno_list[ind], width, height)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画像とアノテーションの前処理を行うクラスDataTransformを作成する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# フォルダ「utils」にあるdata_augumentation.pyからimport。\n",
    "# 入力画像の前処理をするクラス\n",
    "from utils.data_augumentation import Compose, ConvertFromInts, ToAbsoluteCoords, PhotometricDistort, Expand, RandomSampleCrop, RandomMirror, ToPercentCoords, Resize, SubtractMeans\n",
    "\n",
    "\n",
    "class DataTransform():\n",
    "    \"\"\"\n",
    "    画像とアノテーションの前処理クラス。訓練と推論で異なる動作をする。\n",
    "    画像のサイズを300x300にする。\n",
    "    学習時はデータオーギュメンテーションする。\n",
    "\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    input_size : int\n",
    "        リサイズ先の画像の大きさ。\n",
    "    color_mean : (B, G, R)\n",
    "        各色チャネルの平均値。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, input_size, color_mean):\n",
    "        self.data_transform = {\n",
    "            'train': Compose([\n",
    "                ConvertFromInts(),  # intをfloat32に変換\n",
    "                ToAbsoluteCoords(),  # アノテーションデータの規格化を戻す\n",
    "                PhotometricDistort(),  # 画像の色調などをランダムに変化\n",
    "                Expand(color_mean),  # 画像のキャンバスを広げる\n",
    "                RandomSampleCrop(),  # 画像内の部分をランダムに抜き出す\n",
    "                RandomMirror(),  # 画像を反転させる\n",
    "                ToPercentCoords(),  # アノテーションデータを0-1に規格化\n",
    "                Resize(input_size),  # 画像サイズをinput_size×input_sizeに変形\n",
    "                SubtractMeans(color_mean)  # BGRの色の平均値を引き算\n",
    "            ]),\n",
    "            'val': Compose([\n",
    "                ConvertFromInts(),  # intをfloatに変換\n",
    "                Resize(input_size),  # 画像サイズをinput_size×input_sizeに変形\n",
    "                SubtractMeans(color_mean)  # BGRの色の平均値を引き算\n",
    "            ])\n",
    "        }\n",
    "\n",
    "    def __call__(self, img, phase, boxes, labels):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        phase : 'train' or 'val'\n",
    "            前処理のモードを指定。\n",
    "        \"\"\"\n",
    "        return self.data_transform[phase](img, boxes, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 動作の確認\n",
    "\n",
    "# 1. 画像読み込み\n",
    "image_file_path = train_img_list[0]\n",
    "img = cv2.imread(image_file_path)  # [高さ][幅][色BGR]\n",
    "height, width, channels = img.shape  # 画像のサイズを取得\n",
    "\n",
    "# 2. アノテーションをリストに\n",
    "transform_anno = Anno_xml2list(voc_classes)\n",
    "anno_list = transform_anno(train_anno_list[0], width, height)\n",
    "\n",
    "# 3. 元画像の表示\n",
    "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
    "plt.show()\n",
    "\n",
    "# 4. 前処理クラスの作成\n",
    "color_mean = (104, 117, 123)  # (BGR)の色の平均値\n",
    "input_size = 300  # 画像のinputサイズを300×300にする\n",
    "transform = DataTransform(input_size, color_mean)\n",
    "\n",
    "# 5. train画像の表示\n",
    "phase = \"train\"\n",
    "img_transformed, boxes, labels = transform(\n",
    "    img, phase, anno_list[:, :4], anno_list[:, 4])\n",
    "plt.imshow(cv2.cvtColor(img_transformed, cv2.COLOR_BGR2RGB))\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# 6. val画像の表示\n",
    "phase = \"val\"\n",
    "img_transformed, boxes, labels = transform(\n",
    "    img, phase, anno_list[:, :4], anno_list[:, 4])\n",
    "plt.imshow(cv2.cvtColor(img_transformed, cv2.COLOR_BGR2RGB))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasetを作成する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# VOC2012のDatasetを作成する\n",
    "\n",
    "\n",
    "class VOCDataset(data.Dataset):\n",
    "    \"\"\"\n",
    "    VOC2012のDatasetを作成するクラス。PyTorchのDatasetクラスを継承。\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    img_list : リスト\n",
    "        画像のパスを格納したリスト\n",
    "    anno_list : リスト\n",
    "        アノテーションへのパスを格納したリスト\n",
    "    phase : 'train' or 'test'\n",
    "        学習か訓練かを設定する。\n",
    "    transform : object\n",
    "        前処理クラスのインスタンス\n",
    "    transform_anno : object\n",
    "        xmlのアノテーションをリストに変換するインスタンス\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, img_list, anno_list, phase, transform, transform_anno):\n",
    "        self.img_list = img_list\n",
    "        self.anno_list = anno_list\n",
    "        self.phase = phase  # train もしくは valを指定\n",
    "        self.transform = transform  # 画像の変形\n",
    "        self.transform_anno = transform_anno  # アノテーションデータをxmlからリストへ\n",
    "\n",
    "    def __len__(self):\n",
    "        '''画像の枚数を返す'''\n",
    "        return len(self.img_list)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        '''\n",
    "        前処理をした画像のテンソル形式のデータとアノテーションを取得\n",
    "        '''\n",
    "        im, gt, h, w = self.pull_item(index)\n",
    "        return im, gt\n",
    "\n",
    "    def pull_item(self, index):\n",
    "        '''前処理をした画像のテンソル形式のデータ、アノテーション、画像の高さ、幅を取得する'''\n",
    "\n",
    "        # 1. 画像読み込み\n",
    "        image_file_path = self.img_list[index]\n",
    "        img = cv2.imread(image_file_path)  # [高さ][幅][色BGR]\n",
    "        height, width, channels = img.shape  # 画像のサイズを取得\n",
    "\n",
    "        # 2. xml形式のアノテーション情報をリストに\n",
    "        anno_file_path = self.anno_list[index]\n",
    "        anno_list = self.transform_anno(anno_file_path, width, height)\n",
    "\n",
    "        # 3. 前処理を実施\n",
    "        img, boxes, labels = self.transform(\n",
    "            img, self.phase, anno_list[:, :4], anno_list[:, 4])\n",
    "\n",
    "        # 色チャネルの順番がBGRになっているので、RGBに順番変更\n",
    "        # さらに（高さ、幅、色チャネル）の順を（色チャネル、高さ、幅）に変換\n",
    "        img = torch.from_numpy(img[:, :, (2, 1, 0)]).permute(2, 0, 1)\n",
    "\n",
    "        # BBoxとラベルをセットにしたnp.arrayを作成、変数名「gt」はground truth（答え）の略称\n",
    "        gt = np.hstack((boxes, np.expand_dims(labels, axis=1)))\n",
    "\n",
    "        return img, gt, height, width\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[[   0.9417,    6.1650,   11.1283,  ...,  -22.9082,  -13.2200,\n",
       "             -9.4034],\n",
       "          [   6.4367,    9.6600,   13.8283,  ...,  -21.4433,  -18.6500,\n",
       "            -18.2033],\n",
       "          [  10.8833,   13.5500,   16.7000,  ...,  -20.9917,  -24.5250,\n",
       "            -25.1917],\n",
       "          ...,\n",
       "          [ -23.9501,  -14.9000,   -1.7583,  ..., -108.6083, -111.0000,\n",
       "           -117.8083],\n",
       "          [ -28.2816,  -20.1750,   -5.5633,  ..., -104.9934, -111.8350,\n",
       "           -119.0000],\n",
       "          [ -20.4766,  -21.0000,  -12.6334,  ..., -107.1685, -115.7800,\n",
       "           -117.1100]],\n",
       " \n",
       "         [[  25.9417,   30.1650,   35.1283,  ...,  -18.0766,  -14.7250,\n",
       "            -11.8534],\n",
       "          [  31.4367,   33.6600,   37.8283,  ...,  -13.5016,  -10.8250,\n",
       "            -10.3783],\n",
       "          [  35.7917,   37.5500,   40.7000,  ...,  -11.8417,  -13.0750,\n",
       "            -14.0167],\n",
       "          ...,\n",
       "          [  -1.9501,    7.1000,   20.2417,  ..., -101.9083, -102.0000,\n",
       "           -109.7167],\n",
       "          [  -6.2816,    1.8250,   16.4367,  ..., -100.0517, -103.6700,\n",
       "           -111.0000],\n",
       "          [   1.5234,    1.0000,    9.3666,  ..., -102.5017, -107.7800,\n",
       "           -109.1100]],\n",
       " \n",
       "         [[  45.2750,   55.1650,   62.1283,  ...,   12.8501,   22.0550,\n",
       "             27.8166],\n",
       "          [  50.8800,   58.3300,   64.4983,  ...,   15.8351,   21.5150,\n",
       "             22.7967],\n",
       "          [  56.0667,   60.5500,   65.1500,  ...,   15.6416,   14.8250,\n",
       "             14.7083],\n",
       "          ...,\n",
       "          [  36.7166,   43.1000,   56.2417,  ...,  -94.7583,  -96.0000,\n",
       "           -101.9000],\n",
       "          [  32.3850,   37.8250,   52.4367,  ...,  -92.1617,  -96.0000,\n",
       "           -101.8867],\n",
       "          [  40.1900,   37.0000,   45.3666,  ...,  -94.5017,  -99.7800,\n",
       "            -99.1466]]]),\n",
       " array([[ 0.09      ,  0.03003003,  0.998     ,  0.996997  , 18.        ],\n",
       "        [ 0.122     ,  0.56756757,  0.164     ,  0.72672673, 14.        ]]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 動作確認\n",
    "color_mean = (104, 117, 123)  # (BGR)の色の平均値\n",
    "input_size = 300  # 画像のinputサイズを300×300にする\n",
    "\n",
    "train_dataset = VOCDataset(train_img_list, train_anno_list, phase=\"train\", transform=DataTransform(\n",
    "    input_size, color_mean), transform_anno=Anno_xml2list(voc_classes))\n",
    "\n",
    "val_dataset = VOCDataset(val_img_list, val_anno_list, phase=\"val\", transform=DataTransform(\n",
    "    input_size, color_mean), transform_anno=Anno_xml2list(voc_classes))\n",
    "\n",
    "\n",
    "# データの取り出し例\n",
    "val_dataset.__getitem__(1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataLoaderを作成する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def od_collate_fn(batch):\n",
    "    \"\"\"\n",
    "    Datasetから取り出すアノテーションデータのサイズが画像ごとに異なります。\n",
    "    画像内の物体数が2個であれば(2, 5)というサイズですが、3個であれば（3, 5）など変化します。\n",
    "    この変化に対応したDataLoaderを作成するために、\n",
    "    カスタイマイズした、collate_fnを作成します。\n",
    "    collate_fnは、PyTorchでリストからmini-batchを作成する関数です。\n",
    "    ミニバッチ分の画像が並んでいるリスト変数batchに、\n",
    "    ミニバッチ番号を指定する次元を先頭に1つ追加して、リストの形を変形します。\n",
    "    \"\"\"\n",
    "\n",
    "    targets = []\n",
    "    imgs = []\n",
    "    for sample in batch:\n",
    "        imgs.append(sample[0])  # sample[0] は画像imgです\n",
    "        targets.append(torch.FloatTensor(sample[1]))  # sample[1] はアノテーションgtです\n",
    "\n",
    "    # imgsはミニバッチサイズのリストになっています\n",
    "    # リストの要素はtorch.Size([3, 300, 300])です。\n",
    "    # このリストをtorch.Size([batch_num, 3, 300, 300])のテンソルに変換します\n",
    "    imgs = torch.stack(imgs, dim=0)\n",
    "\n",
    "    # targetsはアノテーションデータの正解であるgtのリストです。\n",
    "    # リストのサイズはミニバッチサイズです。\n",
    "    # リストtargetsの要素は [n, 5] となっています。\n",
    "    # nは画像ごとに異なり、画像内にある物体の数となります。\n",
    "    # 5は [xmin, ymin, xmax, ymax, class_index] です\n",
    "\n",
    "    return imgs, targets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([4, 3, 300, 300])\n",
      "4\n",
      "torch.Size([2, 5])\n"
     ]
    }
   ],
   "source": [
    "# データローダーの作成\n",
    "\n",
    "batch_size = 4\n",
    "\n",
    "train_dataloader = data.DataLoader(\n",
    "    train_dataset, batch_size=batch_size, shuffle=True, collate_fn=od_collate_fn)\n",
    "\n",
    "val_dataloader = data.DataLoader(\n",
    "    val_dataset, batch_size=batch_size, shuffle=False, collate_fn=od_collate_fn)\n",
    "\n",
    "# 辞書型変数にまとめる\n",
    "dataloaders_dict = {\"train\": train_dataloader, \"val\": val_dataloader}\n",
    "\n",
    "# 動作の確認\n",
    "batch_iterator = iter(dataloaders_dict[\"val\"])  # イタレータに変換\n",
    "images, targets = next(batch_iterator)  # 1番目の要素を取り出す\n",
    "print(images.size())  # torch.Size([4, 3, 300, 300])\n",
    "print(len(targets))\n",
    "print(targets[1].size())  # ミニバッチのサイズのリスト、各要素は[n, 5]、nは物体数\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5717\n",
      "5823\n"
     ]
    }
   ],
   "source": [
    "print(train_dataset.__len__())\n",
    "print(val_dataset.__len__())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上"
   ]
  }
 ],
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